RidgeRace: ridge regression for continuous ancestral character estimation on phylogenetic trees

Area of Science:

• Evolutionary biology
• Bioinformatics
• Computational biology

Background:

• Ancestral character state reconstruction estimates traits of extinct species using phylogenetic data.
• It provides insights into evolutionary history and can test hypotheses like trait co-evolution.
• Existing methods often assume Brownian motion models for character evolution.

Purpose of the Study:

• To develop and evaluate a novel method for ancestral character state reconstruction.
• To estimate evolutionary rates and ancestral values along phylogenetic trees.
• To demonstrate the method's utility as a feature selection tool in cancer research.

Main Methods:

• The study proposes using ridge regression to infer evolutionary rates and ancestral values.
• The algorithm was implemented in C++ as a standalone program.
• Extensive simulations were conducted to assess the method's performance.

Main Results:

• The proposed ridge regression method achieves competitive accuracy in reconstructing ancestral values.
• Simulations demonstrate the method's effectiveness compared to state-of-the-art software.
• The method was successfully applied to ovarian cancer data for hierarchical clustering and feature selection.

Conclusions:

• Ridge regression offers a robust and accurate approach for ancestral character state reconstruction.
• The method is a valuable tool for evolutionary inference and has practical applications in cancer genomics.
• The freely available software facilitates its adoption in the scientific community.